A Dynamical System Approach to Research in Second Language Acquisition

8/12/2019 A Dynamical System Approach to Research in Second Language Acquisition

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Journal of English LanguageTeaching and Learning

No. 11, 2013

A Dynamical System Approach to Research

in Second Language Acquisition

Hassan SoleimaniAssistant professor, Payame Noor University

Seyyed Mohammad AlaviAssociate professor, Tehran University

Abstract

Epistemologically speaking, second language acquisition research

(SLAR) might be reconsidered from a complex dynamical system view

with interconnected aspects in the ecosystem of language acquisition.

The present paper attempts to introduce the tenets of complex system

theory and its application in SLAR. It has been suggested that the

present dominant traditions in language acquisition research are too

simplistic to delve into the nature of language acquisition. The belief is

that the Newtonian conceptualization of SLA research cannot be

comprehensive to deal with the complexities of language acquisition

research. So the suggested definition for SLA research in the present

paper is a complex dynamical nonlinear open adaptive system of

inquiry to find probable solutions to problems.

Keywords: second language acquisition research; complex system

theory; dynamical; emergent; reductive.

30/5/92 :7/2/92 :-*-E-mail: [email protected]


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128 Journal of English Language Teaching and Learning.No. 11 /Spring &Summer 2013

Introduction

From an ontological perspective, research in education in general

and second language acquisition in particular has witnessed

fluctuations galore. In the milieu of second language acquisition

(SLA), the definition ofresearch in applied linguistics, as with many

other terms, is not clear-cut, and the field is replete with terminology

confusion. Brown (1988) classifies research into two broad categories

as secondary and primary research, each of which subcategorized into

other types. Van Lier (1988) considers educational research in terms of

intervention and selectivity axes. Grotjahn (1988) classifies research in

terms of methods of data collection, data types, and data analysis

procedures. To Larsen-Freeman and Long (1991), research could betaken into account cross-sectional or longitudinal time orientation.

Reichardt and Cook (1979) sum up research types into qualitative and

quantitative paradigms where the former supports a particularistic

perspective and the latter a holistic one. More specifically, Dornyei

(2007) in his brief historical overview of QUAN-QAUL research

paradigms, quotes that quantitative research is closely associated with

numerical values and standardized procedures and so a scientific

method whereas qualitative paradigm is believed to be "open and

fluid" and "without preconceived hypotheses". Mackey and Gass

(2005) equate quantitative research with experimental design and

qualitative research with non-experimental paradigm. All these pave

the way to the point that research is a complex system which needs tobe interpreted in terms of the features of a complex system.

This article is intended to briefly grapple with issues about second

language acquisition research (SLAR) through the lens of recent

advances in dynamical complex system theory. The rationale in the

succinct paper is that research is not a concept to easily arrive at, and

we hope this perspective may help put forward questions about

research differently and more usefully. Using Cummins (1983)

classification of theories into property and transition theories, and

resorting to Larsen-Freeman's (2008) characterization of complex

systems in applied linguistics, to me, second language acquisition

research might be redefined as a complex dynamical nonlinear open

adaptive system of inquiry to find probable solutions to problems.


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A Dynamical System Approach to Research in Second Language ....... 129

Complex system theory: The background

Complexity theory is originating in the natural sciences and applied

in the human sciences. Complexity theory makes an attempt to

expound the way order comes out of chaos in systems. Regarding

living systems, the theory explains the creation of complex adaptive

systems and their existence. Historically speaking, the origin of

complex system theory dates back probably to the meteorologist

Edward Lorenz seminal experiment in 1961 when he had managed to

create a skeleton of a weather system from a handful of differential

equations. Applying computer simulation, he maintained a perpetual

simulation that would produce an output of a day's progress in the

simulation every minute as a line of text on a roll of paper. Lorenzexamined the way an air current would rise and fall while being heated

by the sun. His computer contained the mathematical equations which

governed the flow of the air currents. Because of the deterministic

nature of computer code, Lorenz predicted that by feeding the same

initial values, he would obtain exactly the same result when he ran the

program. However, Lorenz found that when the same initial values

were given, he came into an exactly different result each time. By

closer examination, it was revealed that he was not truly imputing the

same initial values each time; initial values were a little bit different

from each other. The differences were not noticed since they were

unbelievably small, microscopic, and insignificant by usual standards.

The simulation pattern revealed that nothing ever happened the same

way twice, but there was an underlying order. He noticed that a small

change in initial conditions can drastically change the long-term

behavior of a system (known as Lorenz attractor).

Lorenz famous paper entitled "Predictability: Does the flap of the

butterfly's wings in Brazil set off a tornado in Texas?" in 1972 is

associated with butterfly effect orchaos theory. It came to be known

that even the smallest imaginable difference between two sets of initial

conditions would result in a great difference (Gleick, 2008; Stewart,

2002).

In addition, some Nobel laureates including Ilya Prigogine in

chemistry, Kenneth Arrow in economics, and Philip Anderson and


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Murray Gell-Mann in physics are among the advocates of complexitytheory. The potential of complex systems is so great since it deals with

real systems in the real word, say, transportation system, human

immune system, forest, educational systems, weather, and SLAR

indeed. As Gell-Mann (1994) states, although complex system theory

has originated in the natural sciences, it has exciting and useful

contributions to the social and behavioral sciences, and even matters of

policy for human society.

The chemist Ilya Prigogine coined the term dissipative system to

clarify an inherent process quintessential in complex systems. His

proposition is that a dissipative system takes in energy from outside of

itself and self-organizes its pattern. In fact, a dissipative system is open

to the external context and regulates itself to create order. As Larsen-Freeman (2008) quotes him, "the study of dissipative systems focuses

on the interplay between structure, on the one hand, and change (or

dissipation) on the other" (p. 3).

Holland (1995), a biologist and the father of genetic algorithms,

enumerates four properties (aggregation, nonlinearity, flows, and

diversity) and three mechanisms (tagging, internal models, and

building blocks) for each complex adaptive system. Aggregation

implies the way complex systems behave. Complex behaviors emerge

as the result of interactions of less complex agents. To him, for

example, an ant has a stereotypical behavior and usually dies when in

non-normal situations; nevertheless, the ant nest is extremely adaptive

and can generally survive abnormal conditions. In nonlinearity, thebehavior of the whole cannot be reduced to the sum of the parts.

According to nonlinearity, the behavior of complex systems cannot be

taken by the behaviors of individual members. For instance, a watch,

which is a complicated but not complex system, can be understood

based on the interactions of the parts as it is a linear system. The third

feature is flows which refer to the movements of resources among

agents via connectors that change according to the system. For

example, the connectors in a food transportation system are various

vehicles, the resources flowing are the different foods, and the agents

are farmers and grocery stores. The last feature is diversity. One can

see diversity in educational systems where different types of teachers,

staff members, and students interact (Holland, 1995).


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Complex systems involving chaos are against determinism inphilosophy. Determinism is the belief that every event is the

inevitable result of preceding events, and thus every event can be

completely predicted in advance. Determinism in philosophy dates

back to ancient Greece, but its application in science traces back to

1500 A.D. with the idea that a cause-and-effect rule governs all

motions. At the beginning of the 17th

century, Francis Bacon

contributed to the so-called scientific revolution by his empirical

method and his emphasis on reliable knowledge. Bacon suggested

that empirical observation and formal experiments are the real

business of science. Newton's general law of gravitation was

published in 1687 which put forward a coherent explanation of the

movements of the planets (Jordan, 2004). Accordingly, given theinitial conditions (the position and velocity of each body) and the

acting forces, the entire future history of that system is determined

uniquely (Retrieved from

http://www.skidmore.edu/academics/lsi/arcadia/newton.html).

In contrast, chaos could be considered as a superseder for the

Newtonian metaphor of the clockwork predictability, as pointed out by

Waldrop (1992). Instead of explaining the universe as a gigantic clock

which is governed by simple rules, chaos theory metaphor can be

described as a kaleidoscope: the world is a matter of patterns that

change, that partly repeat, but never quite repeat, that are always new

and different (p. 330).

In the 20th century, mechanical determinism was attacked andbroke down gradually. The idea that quantum mechanics is based on

the principle of uncertainty rejected the determinism at a microscopic

level; similarly, the butterfly effect resulted in the denial of the

determinism at a macroscopic level. Based on the Copenhagen

paradigm of quantum mechanics, a microscopic system is considered

as an uncertain wave motion that gets certain merely when a

recognizing subject interferes with the object rather than the object is

basically determinate. An issue of great interest in quantum mechanics

is the principle of superposition. According to this principle, "quantum

mechanics requires that a system exist in a range of possible

statesuntil a measurement is made, at which point one of those states

takes on a definite reality" (Lindley, 1997, p.18). Hence, the core of


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the superposition principle is that an organism exists in more than onestate at any given time. To Niels Bohr, the criterion for everything to

be real is its observability. At the same time, he, nevertheless, stated

that the act of measurement constrains a thing to a single possibility.

Both of these observations are embodied in the principle of

superposition.

Complexity theory and SLA

With the spread of complex system theory in physics, mathematics,

and biology, in the last decade the enthusiasm for its modeling to SLA

context in general and second language acquisition research in

particular has caught the attention of some researchers. It appears thetime is ripe for SLA to follow the empirically based new trend in

science and get divorced from absolutely Newotnian camp of causative

reality and its reductionist positivistic linear tenets. Some scholars

have felt the new conceptualization of science and are heralds of

changes in SLA. Consequently, a few articles and studies have been

published using terminologies as complexity theory, chaos theory,

dynamical system, and complex systems.

Complexity theory is scarcely dealt with in the literature of SLA.

Two seminal articles by Larsen-Freeman (1997) and van Lier (1997)

brought complexity theory into the realm of applied linguistics.

Larsen-Freeman's influential article "Chaos/Complexity Science and

Second Language Acquisition" in Applied Linguistics in 1997introduced the main developments of physical sciences contributing to

the recent developments in academia. She has enumerated the main

features of complex systems: dynamic, complex, nonlinear, chaotic,

unpredictable, sensitive to initial conditions, open, adaptive, and self-

organizing. She also compares complex systems and language in terms

of dynamism and finds numerous commonalities including the fact that

languages grow and change. She draws readers' attention to the

applicability of complex system theory to interlanguage systems of

language learners. Furthermore, to Van Lier (1997), it is essential to

consider second language classroom context as a complex adaptive

system in which the details are all significant. He further maintains that

it is not feasible to search for cause-effect relations in SLA.


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Following the seminal works by Larsen-Freeman and some otherresearchers to introduce Gleicks (1987) Chaos: Towards a New Kind

of Science and Waldrops (1992) Complexity: The Emerging Science at

the Edge of Order and Chaos, today, it appears that complex system

theory has found its way into recent discussions in SLA and applied

linguistics and researchers in the field believe in its role in

interlanguage systems. Later, Bates and Thelen (2003) relates

connectionist theories of mind to complex system theory. Larsen-

Freeman (2000) explains language as a dynamic system which is

composed of numerous components including syntax, semantics,

phonology, morphology, and so forth interacting in non-linear and

unpredictable ways. Larsen-Freeman coined the term grammaring to

describe this dynamic nature of language. Cameron (2003) links thecomplex system theory to discourse and applies the term attractor to

explain discoursal features in language use. Verspoor, Lowie, and van

Dijk (2008) show that examining intra-individual variability in SLA

can provide insight into the dynamics of second language learners. In

their study, using Thelen and Smith's (1994) and van Geert's (1994)

dynamic systems theory paradigm and concepts from microgenetic

variability researches in psychology, they investigated SLA in a rapid

development time period applying advanced visualization techniques.

A case study of a learner displays a general increase over time for the

correlates under study; however, the development is nonlinear, which

reveals moments of progress and regress. The case study sheds light as

well on dynamic interaction of subsystems. In another article, vanGeert (2008), introduces the basic tenets of dynamic system theory and

explains concepts such as time evolution, evolution term, self-

organization, and attractor. Furthermore, the applications of these

concepts in first and second language acquisition are discussed. The

article also expounds the steps necessary to be taken in modeling

dynamic system theory in second language learning. de Bot (2008),

focuses on the development of SLA from the perspective of dynamic

system theory with a focus on development over time. Numerous

examples and applications of dynamic system theory in SLA are given.

The author also offers some possible lines of dynamic-system-theory

based research agendas. Plaza-Pust (2008) examines Universal

Grammar based on dynamic system theory and proposes a dynamic


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approach to the development of grammars. He attributes the observednonlinear behavior to a complex information flow by internal and

external feedback processes. He further argues that changes in

grammars are because of the amplification of new information leading

to system-internal conflicts.

Complex system theory and SLA research

Complexity as a concept in science is not totally new (Sardar &

Abrams, 1999); however, we observe the incarnation of the concept in

natural sciences first and today its emergence in second language

acquisition research. It might be argued that the advent of complexity

in second language acquisition research implies the shift of

paradigm, to use Thomas Kuhns terminology in the philosophy ofscience (as cited in Jordan, 2004; see also Watson -Gegeo, 2004 for

paradigm shift in human and social sciences). Like language, language

acquisition research is a multifaceted phenomenon involving numerous

endogenous and exogenous variables. In the past decades, second

language acquisition is researched from different perspectives:

cognitive, affective, cultural, social, political, ideological, and so forth.

Nevertheless, the attempts made by majority of researchers in the field

have centered around reductionism and separationist linear

conceptualizations in research. If language acquisition is viewed from

an ecological approach in which the affordances in the ecosystem of

language acquisition are all taken into account, complexity theory

finds its way into SLA research paradigms. Therefore, van Liers

(2004) deep ecology conceptualization might be borrowed to explain

the interrelatedness and complexity of all processes involved in second

language acquisition research.

In the following sections, the intention is to argue for a complex

system theory approach to research in second language acquisition

with a critique of the so-called standard scientific research. It appears

that complex system theory attributes (dynamic, complex, nonlinear,

chaotic, self-organizing, unpredictable, and sensitive to initial

conditions) challenges the basic tenets of established practices in

experimental research paradigms. For the purpose of our discussion,


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the elaborations by Larsen-Freeman and Cameron (2008) concerningthe features of a complex system appear plausible and helpful.

SLAR as a system, a complex system

A system is defined as a set of components that work together in a

certain way to produce some overall state (Larsen-Freeman &

Cameron, 2008). We need to differentiate a system from a set since

belonging to a system has an impact upon the features of the

components. For instance, a classroom is a system in which several

components interact: teacher and his/her characteristics, students and

their characteristics, tasks and activities, lessons, teaching materials,

mnemonics, and etc. The quintessential feature of this classroom

system is that the components of the system affect each other, say,teacher's method is influenced by students' characteristics and

classroom atmosphere. Systems could be simple or complex. A simple

system consists of limited number of components with predictable

patterns of behavior. A traffic light system is a simple system of

typically three options (in Iran): green, amber, and red. The pattern of

traffic light as a simple system is unchangeable and therefore a

predictable sequence is followed: motorists know that an amber light

will be followed by a red one which means to stop. A complex

system, in contrast, involves a large number of elements which

interact in different and changing ways. The elements of a complex

system are technically called component agents and component

elements. Agents are animate beings in a system whereas elements areinanimate aspects of a system. In the classroom metaphor, agents are

teachers and pupils, and elements are facilities, equipments, and the

board to list a few. The point here is that the ecosystem of a complex

system is heterogeneous in the sense that it contains miscellaneous

agents, elements, and processes, processes could also be part of

components. So, it could be claimed that in a complex system one can

find both entities and processes.

Considering the above mentioned characteristics of a complex

system, SLAR could be supposed as a complex system. It is a system

inasmuch as it is produced by a host of components to bring up some

overall state, here a solution to a problem. In addition, SLAR is

necessarily a complex system since it involves heterogeneous agents


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(researcher and participants) and elements (treatment, placebo, data,instructional materials, pre- and post-tests to name a few). In addition,

the process component contributes to the complexity of the SLAR

system. Van Lier (2000) takes an ecological approach to language

learning and emphasizes conglomeration of cognitive, social, and

cultural aspects and their interactions in learning atmosphere. The

likely problem with the so-called scientific research paradigms in

language learning is that the ecology of research is limited to merely

cognitive processes; that is, learning is the result of computational

processes in the brain. Bronfenbrenner (1994) proposes a bioecological

model of hierarchically nested ecosystems and a research methodology

for studying language acquisition that contains the notions of person,

process, context, time, and outcome (cited in van Lier, 2000). So froma complex system SLAR, to arrive at meaningful and useful

interpretations of research results, researchers need to consider the

complexity of SLAR in terms of its agents, components, and elements.

SLA research as a dynamic process

A system is defined as dynamic, i.e. a set of variables that interact

over time (de Bot, Lowie, & Verspoor, 2007). To apply the

conceptualization of complex system theory, research in second

language acquisition is dynamic in the sense that it is composed of a

multitude of agents, elements, and variables. In other words, SLAR

might be assumed as a network of agents who are acting in parallel,

competing, cooperating, and responding to the actions of other agents,elements which are interacting in the ecosystem of research milieu, and

variables which are both manipulated and uncontrolled. The agents and

elements are indispensably interconnected and interdependent and act

upon each other over time contributing to the unpredictability and

dynamism of the SLA research practice.

Being so, based on the complex system theory, taking the agents,

elements, and variables action throughout research into account, the

Newtonian separationist simple causal explanation appears

implausible. An underlying assumption in the so-called scientific

research is that in second language acquisition research there exists a

clear beginning and end state. On the contrary, second language

research is dynamic in the sense that it constantly changes overtime.


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Standard research is reductive; Complex system research is emergent

In the previous section, it was argued that SLAR is complex since

it involves heterogeneous agents, elements, and processes. However, it

should be noticed that, as Larsen-Freeman and Cameron (2008) state,

complex does not mean complicated. What makes a system complex is

not merely the existence of a large number of elements. In other words,

the diversity of components does not make a system complex. In fact,

the behavior of a complex system "emerges from the interactions

[emphasis is mine] of its components" (p. 2). The interaction of

elements in a complex system leads to the emergence of new behavior

and self-organization. Because of the interactions among the elements,

they act in response to the feedback they receive which itself leads tochange and adaptation. That is the reason why sometimes complex

systems are also called adaptive systems.

Standard scientific research is based on reductionism in the

philosophy of science. As van Lier (2000) states, the scientific

perspective dominating Western civilization since the days of Galileo

and Descartes has advocated simplification and selection from the

infinite variety of the real world. Jordan (2004) in a review of criteria

for research and theory construction in SLA mentions the Occam's

Razor principle as an essential standard for SLAR. Based on the

principle, the theory which is constructed with the fewest types of

entity is preferred for the reasons of economy. So the recommendation

imposed by reductionism upon standard scientific research is the

selection of the fewest possible number of components in a research

context. In fact, reduction-based research simplifies a system in a

process called idealization. The concept dates back to ancient times

when Plato considered meaning as an idealization which was already

known to the mind independent of the world experience that awakened

it (Weisler & Milekic, 2000). Probably, it might be the reason why

Chomsky mentions "idealized speaker" in his theory of language

acquisition. In addition, Chomsky's data consisted of idealized speech

samples divorced from the localized impacts of specific dialects.

Similarly, some recent research on second language sentence

processing supports the syntax-based approach which considers


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comprehension process as the application of autonomous syntacticprinciples free from pragmatic, contextual, and real-world knowledge

sources (Harrington, 2002).

Idealization and reductionism are quintessence of scientific

experimental research in SLA. Following scientific vigor and flavor of

natural sciences, experimental SLA researchers separate complex

system from its real context and manipulate the research in a clinical

milieu to investigate the targeted aspect. In other words, the so-called

scientific research takes a snapshot of the language at an instant of

time and idealizes away from contextual temporal factors and

components contributing to the whole system.

Complex system SLAR, in contrast, believes in affordance and a

bioecological perspective in second language research. Affordance, aterm coined by the psychologist Gibson in 1979, deals with the

interrelationship between an organism and particular features of its

environment. Van Lier (2000) defines affordance as "a particular

property of the environment that is relevant to an active, perceiving

organism in environment. An affordance affords further action (but

does not cause or trigger it)" (p. 252). To clarify the affordance

concept, he introduces the leaf metaphor in a jungle: the leaves is the

same, and with fixed properties, but different organisms (a tree frog, an

ant, a caterpillar, a spider, and a shaman) in a jungle perceive and act

upon different properties of the leaf. In case of language acquisition,

the environment is replete with language which offers opportunities for

active participating learners. Similarly, a complex system SLARsupports an environment-based research that has the notions of person,

process, context, time, and outcome. Affordance in SLAR is counter to

dismantling subjects from the ecosystem they live in and investigating

them in laboratory. It denotes the reciprocity between subjects in

research and the environment of research. As Haugen (1972), the

credited figure for introducing the ecology of language, proposes, we

need not only the social and psychological states, but also the impact

of environment on subjects engaged in research (cited in Hornberger,

2002).

So in complex system SLAR, we need to consider the whole

ecology of language with all its complexity to arrive at more realistic

interpretations of research results. In this regard, Larsen-Freeman and


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Cameron (2008) deal with the methodological developments of secondlanguage research from the lens of complex system theory and propose

that natural properties of complex systems demand changes in

traditional considerations of the functions and roles of theory,

hypothesis, data, and analysis. They maintain that context is not merely

considered as a backdrop, but rather as a complex system itself which

is related to other complex systems.

Conclusion

In the introductory paper, it was argued that second language

acquisition research is such a complex phenomenon that simple cause-

effect Newtonian research formulations cannot provide us with the true

nature of language acquisition. Second language acquisition research isnot a static phenomenon which might be preplanned to be conducted in

predetermined processes. As Littlewood (2004) argues, what we have

at the present time is middle-level rather than comprehensive theories

of language learning. It appears that complex system theory has the

potential to initiate a comprehensive theory regarding second language

learning in general and SLAR in particular. In conformity with de Bot,

Lowie, Thorne, and Verspoors (2013) argumentation for a dynamical

system as language learning, we might similarly assert that SLAR

contains parts and factors which are changing over time, and the

change happens through interaction with the research milieu and

internal reorganization. Because of the interaction of the contributing

factors over time, prediction of research results based on deterministiclinearity rules is not possible. Second language acquisition is dynamic

in this sense and it requires SLAR stakeholders be cautious concerning

the interpretations from the results obtained.


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A Dynamical System Approach to Research in Second Language Acquisition